Accurately　estimating　the　capacity　degradation　of　lithium-ion　batteries　(LIBs)　is　crucial　for　evaluating　the　status　of　battery　health.　However,　existing　data-driven　battery　state　estimation　methods　suffer　from　fixed　input　structures,　high　dependence　on　data　quality,　and　limitations　in　scenarios　where　only　early　charge-discharge　cycle　data　are　available.　To　address　these　challenges,　we　propose　a　capacity　degradation　estimation　method　that　utilizes　shorter　charging　segments　for　multiple　battery　types.　A　learning-based　model　called　GateCNN-BiLSTM　is　developed.　To　improve　the　accuracy　of　the　basic　model　in　small-sample　scenarios,　we　integrate　a　single-source　domain　feature　transfer　learning　framework　based　on　maximum　mean　difference　(MMD)　and　a　multi-source　domain　framework　using　the　meta-learning　MAML　algorithm.　We　validate　the　proposed　algorithm　using　various　LIB　cell　and　battery　pack　datasets.　Comparing　the　results　with　other　models,　we　find　that　the　GateCNN-BiLSTM　algorithm　achieves　the　lowest　root　mean　square　error　(RMSE)　and　mean　absolute　error　(MAE)　for　cell　charging　capacity　estimation,　and　can　accurately　estimate　battery　capacity　degradation　based　on　actual　charging　data　from　electric　vehicles.　Moreover,　the　proposed　method　exhibits　low　dependence　on　the　size　of　the　dataset,　improving　the　accuracy　of　capacity　degradation　estimation　for　multi-type　batteries　with　limited　data.